Personal Information Assistant

Competence CenterInformation Retrieval and Machine Learning
ContactProf. Dr.-Ing. Sahin Albayrak
Partners: T-SystemsDeutsche Telekom,
Website: pia-services.de


The Personal Information Assistant (PIA) is a tool for researchers to support searching for and working with scientific papers. PIA offers a semantic search, computes personalized recommendations, and continually supplies new information that fits the user’s information need. PIA’s features have been visualized with a web application for scientific research. Users can search for scientific papers provided by ACM and IEEE, and manage their searches and search results. Users may also assign ratings and tags to individual search results, thus allowing the underlying personalization component to learn a user’s interests and preferences for future searches.


Information discovery matching the needs and interests of individual users is a challenging task and plays a central role in the daily life. Unfortunately, most of the time a lot of irrelevant information hides the relevant, search results are sometimes redundant or contradictory, often distributed over multiple sources, thus resulting in difficult and costly retrieval. In order to counteract this information overload, personalized services that collect, filter, prepare and present information from different sources are required.


The goal of the Personal Information Assistant project is to provide a comprehensive agent-based solution for the personalized and device-independent supply of information. The user receives information that is relevant to his personal needs and interests. This includes daily news, background knowledge on work issues, or information on leisure time plans and activities.

Besides a typical web search engine interface, the PIA system allows users to define and save searches which are then continually monitored by search agents for any new developments. The architecture of the PIA system is designed to allow information sources to be flexibly integrated into the system. Information is analyzed and filtered using advanced filtering methods, e.g. content-based or collaborative filtering techniques. The use of multiple filtering techniques, which are guided by integrating user feedback from a learning and user modelling component, guarantees a high accuracy of search results.